Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major paradigm shift for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. The purpose of this research is to further explore the MBSE adoption process in the context of the organization. There were three research objectives designed to address the research question: how does organizational structure influence the adoption and implementation of MBSE? Research objective one was: relate organizational structure characteristics to MBSE adoption and implementation measures. Research objective two was: discover how organizational factors contribute to decisions made and other aspects of the MBSE adoption process. Research objective three was: connect different organizational structure and adoption variables together to derive critical variables in the adoption process.
Research objective one was carried out using a survey as the instrument. The objective of the survey was to examine what the effects of organizational structure are on MBSE adoption and implementation. Organizational structure was represented by seven variables: Size, Formalization, Centralization, Specialization, Vertical Differentiation, Flexibility, and Interconnectedness. These are different characteristics of organizational structure that can be measured on a scale. MBSE adoption and implementation was represented by one adoption and three implementation variables. These include Adoption Process, Maturity of MBSE, Use of MBSE, and Influence on organizational outcomes. A total of 51 survey responses were received that met the inclusion criteria. Factor analysis was done for variables with multi-item measures. The factors were then analyzed using pairwise correlations to determine which relationships were significant. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables. Size and Vertical Differentiation had a negative correlation with Use of MBSE (implementation). Centralization was found to have negative correlations with adoption and implementation. Specialization did not have any significant correlations.
Research objective two utilized semi-structured interviews as the main instrument. Survey participants had the opportunity to provide more detailed explanations of their organizations' experiences in the form of follow-up interviews. Eighteen survey participants agreed to this follow-up interview focused on MBSE adoption. Two of the participants shared failed adoption experiences, with the rest were at various stages of the adoption process. One of the most emergent themes out of the interviews was the idea of integration. Integration needs to occur at the organizational level, and the technical level. The technical level refers to the fact that tools, models, and/or data repositories need to be linked together in some way. Integration also has to occur at the organizational level, because a lot of different functional areas need to come together for MBSE. The way that organizations can address the issue of integration is through coordination mechanisms. The ultimate goal is to achieve implicit coordination through the use of connected models, but getting to that point will require coordination between different subunits. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority.
The final research objective of this work was carried out through a causal analysis using the data obtained from the survey and interviews. The purpose of this analysis was to visualize and better understand the adoption process. According to the calculated measures of centrality, the important nodes in this model are Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools. Improved organizational outcomes is part of a key loop in the causal model. Improved organizational outcomes contributes to leaders and employees' willingness to support and use MBSE methods and tools, which contribute to actual use of tools and methods. This creates more Improved organizational outcomes, completing the loop. The survey results showed that Formalization, Decentralization, Flexibility, and Interconnectedness all have positive correlations with the Influence on organizational outcomes. So these organizational structure components are external factors that can be used to positively impact the adoption loop.
Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structure was shown to have significant correlations with adoption and implementation of MBSE. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Improved organizational outcomes was shown to be a critical variable in the adoption process as an avenue for organizational structure factors to have a positive effect on the adoption process. / Doctor of Philosophy / Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major mindset change for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are.
This research looks into how organizational structure may have an impact on MBSE adoption and implementation. This research was carried out with the use of three different methods: an online survey, semi-structured interviews, and a causal analysis. The data obtained from the survey and interviews was used to construct a causal model depicting the MBSE adoption process.
Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structural variables were shown to have significant correlations with adoption and implementation of MBSE. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables, while Centralization had negative correlations with adoption and implementation. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The causal analysis showed that Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools were the critical variables in the MBSE adoption process.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/112115 |
| Date | 07 October 2022 |
| Creators | Henderson, Kaitlin Anne |
| Contributors | Industrial and Systems Engineering, Salado Diez, Alejandro, Van Aken, Eileen Morton, Kleiner, Brian M., Silingas, Darius |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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